wehub-resource-sync
124 lines
4.0 KiB
C++
124 lines
4.0 KiB
C++
// Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
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//
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// Licensed under the Apache License, Version 2.0 (the "License");
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// you may not use this file except in compliance with the License.
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// You may obtain a copy of the License at
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//
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// http://www.apache.org/licenses/LICENSE-2.0
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//
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// Unless required by applicable law or agreed to in writing, software
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// distributed under the License is distributed on an "AS IS" BASIS,
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// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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// See the License for the specific language governing permissions and
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// limitations under the License.
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#include "paddle/phi/kernels/slice_kernel.h"
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#include "paddle/phi/backends/onednn/onednn_reuse.h"
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#include "paddle/phi/core/kernel_registry.h"
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namespace phi {
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bool SliceCheckIfOneDNNSupport(const KernelContext* ctx) {
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auto x = ctx->InputAt<DenseTensor>(0);
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auto vec_dims = vectorize(x.dims());
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bool all_zero_dims = std::all_of(
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vec_dims.cbegin(), vec_dims.cend(), [](int64_t i) { return i == 0; });
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if (!all_zero_dims && x.mem_desc().get_inner_nblks() == 0) {
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return true;
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}
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return false;
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}
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template <typename T, typename Context>
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void SliceKernel(const Context& dev_ctx,
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const DenseTensor& x,
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const std::vector<int64_t>& axes,
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const IntArray& starts,
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const IntArray& ends,
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const std::vector<int64_t>& infer_flags UNUSED,
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const std::vector<int64_t>& decrease_axis,
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DenseTensor* out) {
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const auto& onednn_engine = dev_ctx.GetEngine();
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auto x_vec_dims = vectorize(x.dims());
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auto starts_vec = starts.GetData();
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auto ends_vec = ends.GetData();
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std::vector<int64_t> offsets(x_vec_dims.size(), 0);
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std::vector<int64_t> slice_dims(x_vec_dims);
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for (size_t i = 0; i < axes.size(); ++i) {
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starts_vec[i] =
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starts_vec[i] < 0 ? x_vec_dims[axes[i]] + starts_vec[i] : starts_vec[i];
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ends_vec[i] = ends_vec[i] < 0 ? x_vec_dims[axes[i]] + ends_vec[i]
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: std::min(ends_vec[i], x_vec_dims[axes[i]]);
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offsets[axes[i]] = starts_vec[i];
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slice_dims[axes[i]] =
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std::max(static_cast<int64_t>(0), ends_vec[i] - starts_vec[i]);
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}
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out->Resize(slice_dims);
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// Note(0x45f): To support slice Tensors with shapes like [0, 0, 0].
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if (!x.initialized()) {
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dev_ctx.Alloc(out, x.dtype());
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out->set_layout(DataLayout::ONEDNN);
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return;
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}
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dnnl::memory::data_type x_type = funcs::ToOneDNNDataType(x.dtype());
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funcs::ReorderOneDNNHandler reorder_handler(
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x_vec_dims, x.dtype(), x_type, onednn_engine);
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auto reorder_src_memory_p = reorder_handler.AcquireSrcMemory(
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x.mem_desc(), funcs::to_void_cast(x.data<T>()));
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auto slice_mem_p = reorder_handler.AcquireSubmemory(
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slice_dims, offsets, reorder_src_memory_p);
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auto reorder_dst_memory_p = reorder_handler.AcquireDstMemory(
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out,
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slice_dims,
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funcs::GetPlainOneDNNFormat(static_cast<int>(x_vec_dims.size())),
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dev_ctx.GetPlace());
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auto reorder_p =
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reorder_handler.AcquireReorder(reorder_dst_memory_p, slice_mem_p);
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auto& astream = OneDNNContext::tls().get_stream();
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reorder_p->execute(astream, *slice_mem_p, *reorder_dst_memory_p);
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std::vector<int64_t> new_out_dims(slice_dims.size() - decrease_axis.size());
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if (new_out_dims.empty()) {
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new_out_dims.emplace_back(1);
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} else {
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for (const auto& axis : decrease_axis) {
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slice_dims[axis] = 0;
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}
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int i = 0;
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for (const auto& slice_dim : slice_dims) {
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if (slice_dim != 0) new_out_dims[i++] = slice_dim;
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}
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}
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astream.wait();
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out->Resize(new_out_dims);
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out->set_mem_desc(reorder_dst_memory_p->get_desc().reshape(new_out_dims));
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}
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} // namespace phi
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PD_REGISTER_KERNEL(slice,
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OneDNN,
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ONEDNN,
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phi::SliceKernel,
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float,
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int8_t,
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uint8_t,
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phi::bfloat16) {
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kernel->check_if_onednn_kernel_support_ = phi::SliceCheckIfOneDNNSupport;
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}
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